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Geometric structures of statistical ...

(1998 :)

Geometric structures of statistical physics, information geometry, and learningSPIGL'20, Les Houches, France, July 27-31 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Geometric structures of statistical physics, information geometry, and learningedited by Frederic Barbaresco, Frank Nielsen.
其他題名:	SPIGL'20, Les Houches, France, July 27-31 /
其他題名:	SPIGL'20
其他作者:	Barbaresco, Frederic.
團體作者:
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xiii, 459 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Statistical physics
電子資源:	https://doi.org/10.1007/978-3-030-77957-3
ISBN:	9783030779573$q(electronic bk.)

Geometric structures of statistical physics, information geometry, and learningSPIGL'20, Les Houches, France, July 27-31 /
Geometric structures of statistical physics, information geometry, and learningSPIGL'20, Les Houches, France, July 27-31 /[electronic resource] :SPIGL'20edited by Frederic Barbaresco, Frank Nielsen. - Cham :Springer International Publishing :2021. - xiii, 459 p. :ill. (some col.), digital ;24 cm. - Springer proceedings in mathematics & statistics,v.3612194-1009 ;. - Springer proceedings in mathematics & statistics ;v.19..

Machine learning and artificial intelligence increasingly use methodological tools rooted in statistical physics. Conversely, limitations and pitfalls encountered in AI question the very foundations of statistical physics. This interplay between AI and statistical physics has been attested since the birth of AI, and principles underpinning statistical physics can shed new light on the conceptual basis of AI. During the last fifty years, statistical physics has been investigated through new geometric structures allowing covariant formalization of the thermodynamics. Inference methods in machine learning have begun to adapt these new geometric structures to process data in more abstract representation spaces. This volume collects selected contributions on the interplay of statistical physics and artificial intelligence. The aim is to provide a constructive dialogue around a common foundation to allow the establishment of new principles and laws governing these two disciplines in a unified manner. The contributions were presented at the workshop on the Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning which was held in Les Houches in July 2020. The various theoretical approaches are discussed in the context of potential applications in cognitive systems, machine learning, signal processing.

ISBN: 9783030779573$q(electronic bk.)

Standard No.: 10.1007/978-3-030-77957-3doiSubjects--Topical Terms:

252537
Statistical physics

LC Class. No.: QC174.7

Dewey Class. No.: 530.13

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